Ontologies have been successfully employed in applications that require semantic information processing. However, traditional ontologies are less suitable to express fuzzy or vague information, which often occurs in human vocabulary as well as in several application domains. In order to deal with such restriction, concepts from fuzzy set theory should be incorporated into ontologies so that it is possible to represent and reason over fuzzy or vague knowledge. In this context, this paper proposes a meta-ontology approach for representing fuzzy ontologies covering fuzzy properties, fuzzy rules, and fuzzy reasoning methods such as classical and general fuzzy reasoning, aiming to support the classification of new individuals based on rules containing fuzzy properties.
Data integration becomes even more necessary given the increasing availability of data from distributed and heterogeneous sources. To address such heterogeneity, crisp ontologies have been employed in order to represent the semantics of integrated data. However, it is interesting to use fuzzy logic concepts in these ontologies for a more expressive representation of vague information relevant to some domains. In this context, this paper presents DISFOQuE system for data integration based on fuzzy ontology, which provides a homogeneous view of data sources and also performs query expansions in order to retrieve more comprehensive answers for the user. We have executed a real experiment in the domain of watershed analysis, which provided a homogeneous view of the watershed data sources and more e®ective answers to researchers.
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